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- Title
Support vector regression: A novel soft computing technique for predicting the removal of cadmium from wastewater.
- Authors
Parveen, Nusrat; Zaidi, Sadaf; Danish, Mohammad
- Abstract
The presence of toxic heavy metals in the wastewater coming from industries is of great concern across the world. In the present work, a novel soft computing technique support vector regression (SVR)technique has been used to predict the removal of cadmium ions from wastewater with agricultural waste 'rice polish' as a low-cost adsorbent, with contact time, initial adsorbate concentration, pH of the medium, and temperature as the independent parameters. The developed SVR-based model has been compared with the widely used multiple regression (MR) model based on the statistical parameters such as coefficient of determination (R²), average relative error (AARE) etc. The prediction performance of SVR-based model has been found to be more accurate and generalized in comparison to MR model with low AARE values of 0.67% and high R² values of 0.9997 while MR model gives an AARE value of 29.27% and 0.2161 as coefficient of determination (R²). Furthermore, it has also been observed that the SVR model effectively predicts the behavior of the complex interaction process of cadmium ions removal from waste water under various experimental conditions.
- Subjects
SUPPORT vector machines; SOFT computing; CADMIUM; SEWAGE; AGRICULTURAL wastes
- Publication
Indian Journal of Chemical Technology, 2020, Vol 27, Issue 1, p43
- ISSN
0971-457X
- Publication type
Article